Transformers belong to a class of machine learning models that use self-attention or the scaled dot-product operation as their primary learning mechanism. Transformers were initially proposed for neural machine translation - one of the most challenging natural language processing (NLP) tasks [1]}. Recently, transformers have been successfully employed to tackle various problems in machine learning and achieve state-of-the-art performance. Apart from classical NLP tasks, examples from other areas include image classification, object detection & segmentation, image & language generation, sequential decision making in reinforcement learning, multi-modal (text, speech, and image) data processing, and analysis of tabular and time-series data. This tutorial paper focuses on time-series analysis using Transformers.
